b'Zhe Li'

research

∙ 03/11/2021

Temporal Action Segmentation from Timestamp Supervision

Temporal action segmentation approaches have been very successful recent...

Zhe Li, et al. ∙

research

∙ 11/01/2020

On the Distribution of SINR for MIMO Systems with Widely Linear MMSE Receivers

The widely linear least mean square error (WLMMSE) receiver has been an ...

Wei Deng, et al. ∙

research

∙ 09/24/2020

Distributed Community Detection for Large Scale Networks Using Stochastic Block Model

With rapid developments of information and technology, large scale netwo...

Shihao Wu, et al. ∙

research

∙ 08/10/2020

A Full Second-Order Analysis of the Widely Linear MVDR Beamformer for Noncircular Signals

A full performance analysis of the widely linear (WL) minimum variance d...

Zhe Li, et al. ∙

research

∙ 07/20/2020

Improving Attention-Based Handwritten Mathematical Expression Recognition with Scale Augmentation and Drop Attention

Handwritten mathematical expression recognition (HMER) is an important r...

Zhe Li, et al. ∙

research

∙ 05/18/2020

Joint Multi-Dimension Pruning

We present joint multi-dimension pruning (named as JointPruning), a new ...

Zechun Liu, et al. ∙

research

∙ 04/06/2020

Robust 3D Self-portraits in Seconds

In this paper, we propose an efficient method for robust 3D self-portrai...

Zhe Li, et al. ∙

research

∙ 03/18/2020

PolyFit: Polynomial-based Indexing Approach for Fast Approximate Range Aggregate Queries

Range aggregate queries find frequent application in data analytics. In ...

Zhe Li, et al. ∙

research

∙ 03/07/2020

RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies

Outlier detection is an important task in data mining and many technolog...

Zhe Li, et al. ∙

research

∙ 03/02/2020

Long Short-Term Sample Distillation

In the past decade, there has been substantial progress at training incr...

Liang Jiang, et al. ∙

research

∙ 11/11/2019

Learning From Brains How to Regularize Machines

Despite impressive performance on numerous visual tasks, Convolutional N...

Zhe Li, et al. ∙

research

∙ 04/12/2019

Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation

Accurate multi-organ abdominal CT segmentation is essential to many clin...

Yuyin Zhou, et al. ∙

research

∙ 02/28/2019

CircConv: A Structured Convolution with Low Complexity

Deep neural networks (DNNs), especially deep convolutional neural networ...

Siyu Liao, et al. ∙

research

∙ 12/12/2018

E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

Recurrent Neural Networks (RNNs) are becoming increasingly important for...

Zhe Li, et al. ∙

research

∙ 09/28/2018

Generative Adversarial Active Learning for Unsupervised Outlier Detection

Outlier detection is an important topic in machine learning and has been...

Yezheng Liu, et al. ∙

research

∙ 08/30/2018

A Unified Analysis of Stochastic Momentum Methods for Deep Learning

Stochastic momentum methods have been widely adopted in training deep ne...

Yan Yan, et al. ∙

research

∙ 06/05/2018

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching

Designing the structure of neural networks is considered one of the most...

Jian Ren, et al. ∙

research

∙ 06/03/2018

An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints

Recently, there emerged revived interests of designing automatic program...

Zhe Li, et al. ∙

research

∙ 05/10/2018

Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing

Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendo...

Zhe Li, et al. ∙

research

∙ 04/11/2018

Learning Topics using Semantic Locality

The topic modeling discovers the latent topic probability of the given t...

Ziyi Zhao, et al. ∙

research

∙ 03/20/2018

Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

Recurrent Neural Networks (RNNs) are becoming increasingly important for...

Zhe Li, et al. ∙

research

∙ 03/20/2018

C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

Increasing malicious users have sought practices to leverage 3D printing...

Zhe Li, et al. ∙

research

∙ 03/14/2018

C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

Recently, significant accuracy improvement has been achieved for acousti...

Shuo Wang, et al. ∙

research

∙ 02/25/2018

A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection

Customer retention is one of the primary goals in the area of customer r...

Kun Hu, et al. ∙

research

∙ 02/18/2018

Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework

Hardware accelerations of deep learning systems have been extensively in...

Yanzhi Wang, et al. ∙

research

∙ 02/15/2018

Image Dataset for Visual Objects Classification in 3D Printing

The rapid development in additive manufacturing (AM), also known as 3D p...

Hongjia Li, et al. ∙

research

∙ 02/03/2018

An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing

With recent trend of wearable devices and Internet of Things (IoTs), it ...

Xiaolong Ma, et al. ∙

research

∙ 11/22/2017

Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

Automatic diagnosing lung cancer from Computed Tomography (CT) scans inv...

Fangzhou Liao, et al. ∙

research

∙ 11/17/2017

Thoracic Disease Identification and Localization with Limited Supervision

Accurate identification and localization of abnormalities from radiology...

Zhe Li, et al. ∙

research

∙ 09/09/2017

A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer

In this paper, we present a simple analysis of fast rates with high pr...

Tianbao Yang, et al. ∙

research

∙ 08/29/2017

CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

Large-scale deep neural networks (DNNs) are both compute and memory inte...

Caiwen Ding, et al. ∙

research

∙ 06/13/2017

SEP-Nets: Small and Effective Pattern Networks

While going deeper has been witnessed to improve the performance of conv...

Zhe Li, et al. ∙

research

∙ 03/12/2017

Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks

Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecede...

Ji Li, et al. ∙

research

∙ 03/01/2017

Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

Recently low displacement rank (LDR) matrices, or so-called structured m...

Liang Zhao, et al. ∙

research

∙ 11/18/2016

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

With recent advancing of Internet of Things (IoTs), it becomes very attr...

Ao Ren, et al. ∙

research

∙ 04/12/2016

Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization

Recently, stochastic momentum methods have been widely adopted in train...

Tianbao Yang, et al. ∙

research

∙ 02/06/2016

Improved Dropout for Shallow and Deep Learning

Dropout has been witnessed with great success in training deep neural ne...

Zhe Li, et al. ∙

Zhe Li

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